{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将feature文件夹的文件重命名为.mat文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007_001761\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007_001630\n",
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      "2011_000412\n",
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      "2007_008204\n",
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      "2011_001613\n",
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      "2010_000814\n",
      "2009_002571\n",
      "2007_008722\n",
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      "2009_002238\n",
      "2010_003473\n",
      "2008_005633\n",
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      "2010_001016\n",
      "2007_003101\n",
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      "2008_000763\n",
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      "2007_003872\n",
      "2010_004520\n",
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      "2010_000284\n",
      "2008_003270\n",
      "2007_003373\n",
      "2011_001534\n",
      "2007_002260\n",
      "2009_002539\n",
      "2010_003971\n",
      "2010_003014\n",
      "2007_009691\n",
      "2010_004772\n",
      "2007_004483\n",
      "2009_000716\n",
      "2010_001908\n",
      "2010_004337\n",
      "2008_005812\n",
      "2008_002623\n",
      "2009_004848\n",
      "2010_002691\n",
      "2010_005063\n",
      "2007_001239\n",
      "2007_005828\n",
      "2007_004722\n",
      "2007_006647\n",
      "2010_002200\n",
      "2011_002040\n",
      "2008_006216\n",
      "2007_004649\n",
      "2007_002266\n",
      "2009_002635\n",
      "2010_001577\n",
      "2010_001251\n",
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      "2010_002623\n",
      "2007_004538\n",
      "2008_000943\n",
      "2007_000762\n",
      "2011_001880\n",
      "2007_000123\n",
      "2007_002445\n",
      "2011_001794\n",
      "2007_003499\n",
      "2007_003621\n",
      "2010_003231\n",
      "2007_005915\n",
      "2010_001734\n",
      "2010_005871\n",
      "2009_000205\n",
      "2010_005706\n",
      "2007_003169\n",
      "2007_002427\n",
      "2009_000446\n",
      "2007_005626\n",
      "2009_004568\n",
      "2009_004987\n",
      "2009_001433\n",
      "2008_003155\n",
      "2011_002379\n",
      "2008_002383\n",
      "2007_002619\n",
      "2011_002244\n",
      "2007_003786\n",
      "2007_009841\n",
      "2010_002988\n",
      "2010_000427\n",
      "2008_001885\n",
      "2008_004552\n",
      "finished!\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "path='/home/dl/deeplab_v2/voc2012/features/deeplab_largeFOV/val/fc8'\n",
    "files=os.listdir(path)\n",
    "for afile in files:\n",
    "    file_path=os.path.join(path,afile)\n",
    "    if os.path.isfile(file_path):\n",
    "        if os.path.getsize(file_path)==0:\n",
    "            continue\n",
    "        mat_idx=afile[:afile.find('.mat')]\n",
    "        print mat_idx\n",
    "        os.renames(os.path.join(path,mat_idx+\".mat\"),os.path.join(path,mat_idx+\"_blob_0.mat\"))\n",
    "print \"finished!\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将对应feature文件夹的ppm文件拷贝到另一个文件夹，因为densecrf的运行。需要ppm和.mat文件数量一致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '/home/dl/DL_dataset/images_aug_ppm/2007_007524.ppm'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-583ee75b0eb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mmat_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mafile\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mafile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_blob_0.mat'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_file\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmat_idx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\".ppm\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget_file\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmat_idx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\".ppm\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0mmat_idx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/shutil.pyc\u001b[0m in \u001b[0;36mcopy\u001b[0;34m(src, dst)\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mdst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mcopyfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mcopymode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/shutil.pyc\u001b[0m in \u001b[0;36mcopyfile\u001b[0;34m(src, dst)\u001b[0m\n\u001b[1;32m     80\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mSpecialFileError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`%s` is a named pipe\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfsrc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m             \u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/home/dl/DL_dataset/images_aug_ppm/2007_007524.ppm'"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import shutil\n",
    "path='/home/dl/DL_dataset/test_dense/fc8'\n",
    "files=os.listdir(path)\n",
    "source_file=\"/home/dl/DL_dataset/images_aug_ppm/\"\n",
    "target_file=\"/home/dl/DL_dataset/test_dense/PPMImages/\"\n",
    "a = 0\n",
    "for afile in files:\n",
    "    file_path=os.path.join(path,afile)\n",
    "    if os.path.isfile(file_path):\n",
    "        if os.path.getsize(file_path)==0:\n",
    "            continue\n",
    "        mat_idx=afile[:afile.find('_blob_0.mat')]\n",
    "        shutil.copy(source_file+mat_idx+\".ppm\",target_file+mat_idx+\".ppm\")\n",
    "        print mat_idx\n",
    "        a=a+1\n",
    "print a\n",
    "print \"finished!\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将dense crf生成的bin文件转为图片png的格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(375012,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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XcsOjRdOu23ISrnoeQ4Op9xhs1YAq91PmVAlUpVUNxOqqVE0PSLu2A5rSqgZwq6SdSrLz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c704635d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.misc import imsave\n",
    "filepath='/home/dl/deeplab_v2/voc2012/res/features/deeplab_largeFOV/val/fc8/post_densecrf_W4_XStd49_RStd5_PosW3_PosXStd3/'\n",
    "f = open(filepath+'2010_003183.bin', mode='rb')\n",
    "x = np.fromfile(f, dtype=np.ubyte)\n",
    "print x.shape\n",
    "x = x[0:263169]\n",
    "#x = x[1920:3840]\n",
    "#x = x[3840:5760]\n",
    "x = x.reshape(513,513)\n",
    "#print((x))\n",
    "\n",
    "plt.imshow(x)\n",
    "plt.axis('off')  # clear x- and y-axes\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
